def read_tree_no_labels(parents):
nodes = {}
#parents = [p - 1 for p in parents] # 1-indexed
#print parents, len(parents)
max_degree = 1
for i in xrange(len(parents)):
#print 'visit node:', i
if i not in nodes:
idx = i
prev = None
while True:
#print 'read tree! idx:', idx
node = tree_rnn.Node(
val=idx, origin_idx=idx) # for now, val is just idx
if prev is not None:
assert prev.val != node.val
node.add_child(prev)
nodes[idx] = node
#print 'nodes:', nodes.keys()
parent = parents[idx]
if parent in nodes:
#assert len(nodes[parent].children) < 2
# Note: in our setting, a parent not nessacerily has only 2 children
nodes[parent].add_child(node)
if len(nodes[parent].children) > max_degree:
max_degree = len(nodes[parent].children)
break
elif parent == -1:
root = node
break
prev = node
idx = parent
# ensure tree is completely binary
'''for node in nodes.itervalues():
if not node.children:
continue
assert len(node.children) == 2
'''
# overwrite vals to match sentence indices -
# only leaves correspond to sentence tokens
#leaf_idx = 0
#for node in nodes.itervalues():
# if node.children:
# node.val = None
# else:
# node.val = leaf_idx
# leaf_idx += 1
return root, max_degree
def read_tree(list, vocab):
att_list = []
nodes = []
root = None
for i in range(len(list)):
att_list.append(list[i].split())
word = att_list[i][1]
val = vocab.index(word)
nodes.append(tree_rnn.Node(val))
for i in range(len(list)):
parent = int(att_list[i][6]) - 1
if parent >= 0:
nodes[parent].add_child(nodes[i])
elif parent == -1:
root = nodes[i]
return root
def read_tree(parents, labels):
nodes = {}
parents = [p - 1 for p in parents] # 1-indexed
for i in xrange(len(parents)):
if i not in nodes:
idx = i
prev = None
while True:
node = tree_rnn.Node(val=idx) # for now, val is just idx
if prev is not None:
assert prev.val != node.val
node.add_child(prev)
node.label = labels[idx]
nodes[idx] = node
parent = parents[idx]
if parent in nodes:
nodes[parent].add_child(node)
break
elif parent == -1:
root = node
break
prev = node
idx = parent
# ensure tree is connected: by confirming that there is only one root
num_roots = sum(node.parent is None for node in nodes.itervalues())
assert num_roots == 1, num_roots
# overwrite vals to match sentence indices -
# only leaves correspond to sentence tokens
leaf_idx = 0
for node in nodes.itervalues():
if node.children:
node.val = None
else:
node.val = leaf_idx
leaf_idx += 1
max_degree = max(len(node.children) for node in nodes.itervalues())
# max_degree is the maximum number of children of a node
return max_degree, root
def read_tree(parents, labels):
nodes = {}
parents = [p - 1 for p in parents] # 1-indexed
for i in xrange(len(parents)):
if i not in nodes:
idx = i
prev = None
while True:
node = tree_rnn.Node(val=idx) # for now, val is just idx
if prev is not None:
assert prev.val != node.val
node.add_child(prev)
node.label = labels[idx]
nodes[idx] = node
parent = parents[idx]
if parent in nodes:
assert len(nodes[parent].children) < 2
nodes[parent].add_child(node)
break
elif parent == -1:
root = node
break
prev = node
idx = parent
# ensure tree is completely binary
for node in nodes.itervalues():
if not node.children:
continue
assert len(node.children) == 2
# overwrite vals to match sentence indices -
# only leaves correspond to sentence tokens
leaf_idx = 0
for node in nodes.itervalues():
if node.children:
node.val = None
else:
node.val = leaf_idx
leaf_idx += 1
return root
def test_irregular_tree():
model = DummyTreeRNN(8, 2, 2, 1, degree=4, irregular_tree=True)
emb = model.embeddings.get_value()
root = tree_rnn.Node(3)
c1 = tree_rnn.Node(1)
c2 = tree_rnn.Node(2)
c3 = tree_rnn.Node(3)
c4 = tree_rnn.Node(4)
c5 = tree_rnn.Node(5)
c6 = tree_rnn.Node(6)
root.add_children([c1, c2, c3, c4])
c1.add_children([c5])
c5.add_children([c6])
root_emb = model.evaluate(root)
expected = emb[3] + emb[2] * emb[3] * emb[4] * (emb[1] + emb[5] + emb[6])
assert_array_almost_equal(expected, root_emb)
def read_tree(list, vocab):
att_list = []
nodes = []
root = None
for i in range(len(list)):
att_list.append(list[i].split())
word = att_list[i][1]
tag = att_list[i][3]
if vocab is None:
val = word
tag_idx = 0
else:
val = vocab.index(word)
tag_idx = vocab.indexoftag(tag)
nodes.append(tree_rnn.Node(val, i, tag_idx))
for i in range(len(list)):
parent = int(att_list[i][6]) - 1
if parent >= 0:
nodes[parent].add_child(nodes[i])
elif parent == -1:
root = nodes[i]
return root
def test_tree_rnn():
model = DummyTreeRNN(8, 2, 2, 1, degree=2)
emb = model.embeddings.get_value()
root = tree_rnn.Node(3)
c1 = tree_rnn.Node(1)
c2 = tree_rnn.Node(2)
root.add_children([c1, c2])
root_emb = model.evaluate(root)
expected = emb[3] + emb[1] * emb[2]
assert_array_almost_equal(expected, root_emb)
cc1 = tree_rnn.Node(5)
cc2 = tree_rnn.Node(2)
c2.add_children([cc1, cc2])
root_emb = model.evaluate(root)
expected = emb[3] + (emb[2] + emb[5] * emb[2]) * emb[1]
assert_array_almost_equal(expected, root_emb)
ccc1 = tree_rnn.Node(5)
ccc2 = tree_rnn.Node(4)
cc1.add_children([ccc1, ccc2])
root_emb = model.evaluate(root)
expected = emb[3] + (emb[2] + (emb[5] + emb[5] * emb[4]) * emb[2]) * emb[1]
assert_array_almost_equal(expected, root_emb)
# check step works without error
model.train_step(root, np.array([0]).astype(theano.config.floatX))
# degree > 2
model = DummyTreeRNN(10, 2, 2, 1, degree=3)
emb = model.embeddings.get_value()
root = tree_rnn.Node(0)
c1 = tree_rnn.Node(1)
c2 = tree_rnn.Node(2)
c3 = tree_rnn.Node(3)
root.add_children([c1, c2, c3])
cc1 = tree_rnn.Node(1)
cc2 = tree_rnn.Node(2)
cc3 = tree_rnn.Node(3)
cc4 = tree_rnn.Node(4)
cc5 = tree_rnn.Node(5)
cc6 = tree_rnn.Node(6)
cc7 = tree_rnn.Node(7)
cc8 = tree_rnn.Node(8)
cc9 = tree_rnn.Node(9)
c1.add_children([cc1, cc2, cc3])
c2.add_children([cc4, cc5, cc6])
c3.add_children([cc7, cc8, cc9])
root_emb = model.evaluate(root)
expected = \
emb[0] + ((emb[1] + emb[1] * emb[2] * emb[3]) *
(emb[2] + emb[4] * emb[5] * emb[6]) *
(emb[3] + emb[7] * emb[8] * emb[9]))
assert_array_almost_equal(expected, root_emb)
# check step works without error
model.train_step(root, np.array([0]).astype(theano.config.floatX))
